Morocco buildings

In [1]:

import leafmap.foliumap as leafmap

import leafmap.foliumap as leafmap

In [2]:

def google_buildings_csv_to_vector(filename, output=None, **kwargs):
    """
    Convert a CSV file containing Google Buildings data to a GeoJSON vector file.

    Args:
        filename (str): The path to the input CSV file.
        output (str, optional): The path to the output GeoJSON file. If not provided, the output file will have the same
            name as the input file with the extension changed to '.geojson'.
        **kwargs: Additional keyword arguments that are passed to the `to_file` method of the GeoDataFrame.

    Returns:
        None
    """
    import os
    import pandas as pd
    import geopandas as gpd
    from shapely import wkt

    df = pd.read_csv(filename)

    # Create a geometry column from the "geometry" column in the DataFrame
    df["geometry"] = df["geometry"].apply(wkt.loads)

    # Convert the pandas DataFrame to a GeoDataFrame
    gdf = gpd.GeoDataFrame(df, geometry="geometry")
    gdf.crs = "EPSG:4326"

    if output is None:
        output = os.path.splitext(filename)[0] + ".geojson"

    gdf.to_file(output, **kwargs)

def google_buildings_csv_to_vector(filename, output=None, **kwargs): """ Convert a CSV file containing Google Buildings data to a GeoJSON vector file. Args: filename (str): The path to the input CSV file. output (str, optional): The path to the output GeoJSON file. If not provided, the output file will have the same name as the input file with the extension changed to '.geojson'. **kwargs: Additional keyword arguments that are passed to the `to_file` method of the GeoDataFrame. Returns: None """ import os import pandas as pd import geopandas as gpd from shapely import wkt df = pd.read_csv(filename) # Create a geometry column from the "geometry" column in the DataFrame df["geometry"] = df["geometry"].apply(wkt.loads) # Convert the pandas DataFrame to a GeoDataFrame gdf = gpd.GeoDataFrame(df, geometry="geometry") gdf.crs = "EPSG:4326" if output is None: output = os.path.splitext(filename)[0] + ".geojson" gdf.to_file(output, **kwargs)

For example, download the Morocco Buildings Footprint impacted by the earthquake from https://data.humdata.org/dataset/openbuildings_morocco_earthquake_footprint

In [3]:

url = 'https://data.humdata.org/dataset/c6059279-4521-4b39-8b18-d43aedc012c3/resource/6af2ee44-1807-4fb7-a647-a42fc7ffbff0/download/open_buildings_v3_morocco_epicenter.csv.gz'

url = 'https://data.humdata.org/dataset/c6059279-4521-4b39-8b18-d43aedc012c3/resource/6af2ee44-1807-4fb7-a647-a42fc7ffbff0/download/open_buildings_v3_morocco_epicenter.csv.gz'

In [4]:

filename = 'open_buildings_v3_morocco_epicenter.csv.gz'

filename = 'open_buildings_v3_morocco_epicenter.csv.gz'

In [5]:

# leafmap.download_file(url, filename) #1.2 GB

# leafmap.download_file(url, filename) #1.2 GB

The following code cell takes 20-30 minutes to run. Please be patient.

In [6]:

# google_buildings_csv_to_vector(filename, output='buildings.gpkg')

# google_buildings_csv_to_vector(filename, output='buildings.gpkg')

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Last update: 2023-09-20